How Do Urban Parks Provide Bird Habitats and Birdwatching Service? Evidence from Beijing, China - MDPI

remote sensing
Article
How Do Urban Parks Provide Bird Habitats and
Birdwatching Service? Evidence from Beijing, China
Zhengkai Zhang 1,2 and Ganlin Huang 1,2, *
 1 Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface
 Processes and Resource Ecology (ESPRE), Beijing Normal University, No 19 Xinjiekouwai Road,
 Beijing 100875, China; zkzhang@mail.bnu.edu.cn
 2 School of Natural Resources, Faculty of Geographical Science, Beijing Normal University,
 Beijing 100875, China
 * Correspondence: ghuang@bnu.edu.cn
 



 Received: 7 August 2020; Accepted: 23 September 2020; Published: 27 September 2020 


 Abstract: Parks are an important green infrastructure. Besides other benefits for human and
 animals, parks provide important bird habitats and accommodate most human-bird interactions
 in cities. Understanding the complex dynamics among park characteristics, bird habitats and park
 attractiveness to birdwatchers will inform park designers and managers. However, previous studies
 often examined factors influencing bird habitats and birdwatching activities separately. To fill this
 gap, we aim to study the whole picture of “parks, birds and birdwatchers” in Beijing, China for its
 spatial patterns and possible factors which influence bird habitat areas and birdwatching services.
 We conducted a three-month bird census in at 159 sites and mapped bird habitat areas in parks
 of Beijing through the maximum entropy method based on results of the bird survey as well as
 high-resolution remote sensing data. We derived the number of birdwatching records to describe
 birdwatching activities from the China Birdwatching Record Center website. We used correlation
 analysis, regression and analysis of variance to investigate factors that may influence areas of bird
 habitats and the number of birdwatching records for each park. Our results showed that among the
 102 parks, 61 provide habitats to breeding birds with an average of 17 ha, and 26 parks generated a
 total of 330 birdwatching records. Park size, age, proportion of pavement, landscape connectedness,
 pavement largest patch index and woodland patch density explained 95% of the variation in habitat
 areas altogether. Bird habitat area alone explained 65% of the variation in the number of birdwatching
 records. Furthermore, parks with birdwatching records are significantly larger, older, closer to the city
 center and more accessible than those have no reported birdwatching. These findings have important
 implications for park management. While park size or age cannot be easily changed, modifying
 landscape patterns can increase bird habitats in parks, and improving accessibility may attract more
 birdwatchers to parks that already have considerable bird habitats.

 Keywords: urban bird; urban park; birdwatching; MaxEnt modeling; landscape pattern;
 ecosystem services

1. Introduction
 Cities are densely populated and highly modified ecosystems [1], where plants and animals
provide important ecosystem services to urban residents [1]. Birds, in particular, play an essential
role in the wellbeing of urban dwellers. Researchers found that the presence of birds significantly
contributes to people’s mental health and self-rated wellbeing [2,3]. For example, birds’ colorful look
was found to deliver aesthetic enjoyment and make people feel pleasant [4,5]. Moreover, birdsong
diversity increased people’s self-reported wellbeing and appreciation of the landscape [6,7].

Remote Sens. 2020, 12, 3166; doi:10.3390/rs12193166 www.mdpi.com/journal/remotesensing

Remote Sens. 2020, 12, 3166 2 of 15

 Parks are important bird habitats in urban areas, providing food [8] and nesting places [9]. At the
regional scale, urban parks form habitat islands in built-up areas, which connect with other vegetation
patches and constitute a “habitat network” [1,10,11]. Furthermore, parks in cities along the major
migratory bird flyways are important stopover places for migrant birds [12,13].
 Parks also accommodate most human–bird interactions in cities, including causal bird encountering
as well as birdwatching. Compared with bird habitats in remotely located areas, urban parks are more
frequently visited [14,15]. People can easily get close to birds and observe many different types of birds
in one place in the park [16]. Consequently, parks are attractive to birdwatchers [14,17]. In addition,
birdwatching organizations often organize birdwatching tours in urban parks, which contribute to
increased birdwatching activities in parks [18,19].
 Due to the essential roles which urban parks play for both birds and human–bird interactions,
researchers have been eager to understand what factors may affect urban parks’ capacities to provide
bird habitats and accommodate human–bird interactions. Existing studies revealed that many factors
contributed to park bird diversity (species richness or species α diversity), such as park size [9,16,20–23],
park age [24,25], tree cover [26], habitat heterogeneity [16,24], landscape connectedness [16,27]
and waterbody area [16]. In contrast, other factors may weaken bird diversity in parks, such as the
density of buildings and pavements [22,28,29], urbanization degree [20,23,30], pedestrian rate and
noise level around the park [9,20,31]. In contrast, relatively little attention has been paid to how much
habitat parks can provide for birds. While few papers have previously looked at distribution models
for birds in urban green infrastructure [21,32–34], parks were either not considered [21,33] or regarded
as one type of land use [32,34].
 Locations in which birdwatching activities may take place have been studied in protected
areas and national parks instead of urban parks [17,35,36]. Researchers found that bird species
richness [17,36], rare bird presence [17,35] and accessibility [15,17] were the most important factors
attracting birdwatchers. It remains unclear how birdwatching, as a typical bird–human interaction,
is associated with bird habitat in urban parks.
 Vegetation remote sensing has been used in various fields, such as vegetation cover classification
[37–39], land surface temperature pattern analysis [40–44], species habitat characteristics [45–47]
or identification [48–52] and ecosystem services assessment [53,54]. Predicting the habitat of the species
and estimating the quality of the habitat is necessary to urban planning, green space management and
biodiversity protection. However, it was seldom used in urban areas. Many models can be used to
estimate wildlife habitats, including MaxEnt [32,55]. MaxEnt is a niche-based model [56,57] and has
been widely used to estimate species distribution for conservation purposes [55,58] or species invasion
risk [59,60]. MaxEnt model uses species presence-only data and a set of environmental indicators as
input to produce a pixel-based probability map of species distribution as output [56]. MaxEnt has been
applied in urban areas to model suitable habitats for birds [32]. Using MaxEnt to model bird habitat
will benefit urban green infrastructure biodiversity protection and ecosystem services and provide a
scientific basis for urban green infrastructure planning and designing.
 Considering the essential role which urban parks play in providing bird habitats and hosting
birdwatching activities, relatively few researchers have studied the whole picture of “parks, birds and
birdwatchers” or examined the complex dynamics among them. This paper aims to explore the spatial
patterns of bird habitats and birdwatching activities, as well as potential factors which may influence
them. This study aims to explore these questions in the parks of Beijing, China. Specifically, we ask
two questions: (1) What are the spatial patterns of parks’ bird habitats and birdwatching activities in
Beijing? (2) What factors may affect the amount of habitats and birdwatching services provided by
urban parks?

Remote Sens. 2020, 12, 3166 3 of 15

2. Materials and Methods

2.1. Study Area
 Remote
 WeSens.
 focused 11, x study
 2020, our FOR PEER REVIEW
 in the urban area of Beijing, defined as the areas within the fifth ring road 3 of 16
(Figure 1). It has a total area of 66,600 ha [61], with a population of 10.5 million [62]. This is the most
 densely
densely populatedarea
 populated areaininBeijing.
 Beijing. The
 The average
 average population
 population density
 density waswas157
 157people/ha.
 people/ha.There
 Thereareare317
 urban parks in Beijing, which received 250 million visits in 2010 [63]. We chose 102
317 urban parks in Beijing, which received 250 million visits in 2010 [63]. We chose 102 urban parks [64] urban parks [64]
 (Figure
(Figure 1) 1)
 as as
 ourour study
 study area,
 area, which
 which received
 received thethe majority
 majority of of visits
 visits [65].
 [65]. ForFor example,
 example, inin our
 our study
 study area,
 area,
 the eleven parks funded and managed by the city received 94 million visits in 2017
the eleven parks funded and managed by the city received 94 million visits in 2017 [66]. The 102 parks [66]. The 102 parks
 vary
vary substantially
 substantially in in terms
 terms of of size
 size andand park
 park age.
 age. The
 The total
 total area
 area of of a park
 a park ranges
 ranges from
 from less
 less than
 than 1 ha
 1 ha to to
 nearly
nearly 400400
 ha.ha. The
 The oldest
 oldest parks
 parks were
 were built
 built hundreds
 hundreds ofof years
 years ago
 ago asas imperial
 imperial gardens,
 gardens, while
 while thethe new
 new
 ones were built recently (i.e., Olympic Forest
ones were built recently (i.e., Olympic Forest Park). Park).

 Figure 1. The location of urban parks within the fifth ring road of Beijing.
 Figure 1. The location of urban parks within the fifth ring road of Beijing.
 Beijing has a total of 21 orders, 74 families, 423 species and 173 breeding birds [67]. Among the
423 birdBeijing has
 species in aBeijing,
 total of3021species
 orders,were
 74 families,
 listed as423 species and
 threatened 173 by
 species breeding birds [67]. Union
 the International Among forthe
 423 bird species
Conservation in Beijing,
 of Nature 30 species
 [68]. The were listed
 first organized as threatened
 birdwatching species
 activity by thetook
 in China International Unioninfor
 place in Beijing
 Conservation
1996 of Naturebirdwatching
 [69,70]. Nowadays, [68]. The firstisorganized
 a popularbirdwatching
 activity. Everyactivity
 week,inthere
 China took
 is at place
 least oneinpublic
 Beijing
 in 1996 [69,70].
birdwatching Nowadays,
 event taking placebirdwatching
 in the parksisofa popular activity.With
 Beijing [71,72]. Every week, there
 a variety is atabundant
 of parks, least one public
 bird
 birdwatching event taking place in the parks of Beijing [71,72]. With a variety of parks,
species and vigorous birdwatching activities, Beijing provides a suitable study area to explore our abundant bird
 speciesquestions.
research and vigorous birdwatching activities, Beijing provides a suitable study area to explore our
 research questions.
2.2. Data
 2.2. Data
2.2.1. Bird Census
 2.2.1.
 WeBird Censusour bird census survey in seven parks. We selected the seven parks as samples
 conducted
 We 102
out of the conducted our bird census
 parks, considering theirsurvey in sevenand
 size, location parks.
 age We selected
 (Figure the seven
 1, Table S1). Weparks as samples
 conducted a
 out of the 102 parks, considering their size, location and age (Figure 1, Table S1). We conducted
three-month bird survey during the breeding season, March–July 2017. For each park, we designed a a
 three-month bird survey during the breeding season, March–July 2017. For each park, we designed
 a survey route along the road, which covered most park areas. Along the route, we selected
 observation sites every 150 m for parks smaller than 150 ha and every 200 m for those larger than 200
 ha (Figure 2). As a result, we had 159 sample points in total. We used the sample point count method
 in the bird census [73]. At each sample point, we stayed for five minutes and counted the species and

Remote Sens. 2020, 12, 3166 4 of 15

survey route along the road, which covered most park areas. Along the route, we selected observation
sites every 150 m for parks smaller than 150 ha and every 200 m for those larger than 200 ha (Figure 2).
As a result, we had 159 sample points in total. We used the sample point count method in the bird
census [73]. At each sample point, we stayed for five minutes and counted the species and the number
ofRemote Sens. 2020,
 individual 11, xsited
 birds FOR PEER REVIEW
 within 4 of 16
 a 25 m radius. We conducted each survey within the first four hours
after sunrise.

 Figure 2. Sample sites of bird census.

2.2.2. Possible Influencing Factors: Figure
 Park Characteristics
 2. Sample sites ofand
 birdLand Cover
 census.
 We considered factors which may influence bird habitats and birdwatching from several aspects:
 2.2.2. Possible Influencing Factors: Park Characteristics and Land Cover
accessibility and urbanization degree of a park and its surrounding area, park size and age and the
 We considered
landscape composition factors which may influence
 and configuration in a park bird habitats
 (Table S2). and birdwatching from several aspects:
 accessibility and urbanization
 We used high degree
 spatial resolution of acover
 land park data
 and its surrounding
 extracted area,
 from the park size
 Pleiades and age
 satellite and the
 imagery to
 landscape
quantify thecomposition
 compositionand andconfiguration
 configuration inofa park
 park(Table S2). These landscape composition and
 landscapes.
 We used
configuration high spatial
 variables wereresolution
 further used land cover data extracted
 to investigate fromassociation
 their potential the Pleiades satellite
 with imagery
 bird habitat to
 area
 quantify
and number the
 of composition
 birdwatchingand configuration
 records in parks. The of park landscapes.
 Pleiades image was These landscape
 acquired on 19composition and
 September 2015.
 configuration variables were further used to investigate their potential association
It has one 0.5 m spatial resolution panchromatic band (470–830 nm) and four multispectral bands with bird habitat
 area2and
with number
 m spatial of birdwatching
 resolution records in
 (blue: 430–550 parks.
 nm; The500–620
 green: Pleiadesnm; image was
 red: acquired
 590–710 onnear-infrared:
 nm; 19 September
 2015. Itnm).
740–940 has The
 one image
 0.5 m spatial resolution panchromatic
 was preprocessed using ERDASband TM (470–830
 . The nm) and
 land cover four multispectral
 classification included
 bands with 2 m spatial resolution (blue: 430–550 nm; green: 500–620 nm;
seven types: woodland (i.e., woody vegetation), grassland (i.e., herbaceous vegetation), building, red: 590–710 nm; near-
 infrared: 740–940 nm). The image was preprocessed using ERDAS TM. The land cover classification
road, pavement, waterbody and bare land [61]. An object-based approach was used for the land cover
 included seven
classification. types:
 We first woodland
 segmented the(i.e.,
 imagewoody vegetation),
 into objects, usinggrassland
 the fractal(i.e.,
 net herbaceous vegetation),
 evolution approach that
 building, road, pavement, waterbody andTM bare land [61]. An object-based
is embedded in the software eCognition . The segmentation algorithm uses a bottom-up region approach was used for the
 land cover classification. We first segmented the image into objects, using
merging method, starting with each pixel in the image as an individual segment which is then mergedthe fractal net evolution
 approach
into that isAfter
 larger ones. embedded in the software
 segmentation, the image eCognition TM. The segmentation algorithm uses a bottom-
 objects were then classified using a rule-based classifier
 up region
built merging
 on a class method,
 hierarchy and starting
 associatedwith each pixel
 features and in the for
 rules image as an individual
 classifications segment which
 [61]. Extensive manual is
 then merged into larger ones. After segmentation, the image objects were then classified
editing was conducted to further refine the classification. The overall accuracy of the classification was using a rule-
 based classifier built on a class hierarchy and associated features and rules for classifications [61].
84.56%, with a Kappa value of 0.82. The producer’s and user’s accuracies ranged from 71.43% to 100%
 Extensive manual editing was conducted to further refine the classification. The overall accuracy of
and from 81.08% to 88.24%, respectively.
 the classification was 84.56%, with a Kappa value of 0.82. The producer’s and user’s accuracies ranged
 In addition, we also used medium resolution (30 m) Landsat ETM+ images to calculate the
 from 71.43% to 100% and from 81.08% to 88.24%, respectively.
normalized difference vegetation index (NDVI) and a Digital Elevation Model (DEM 30) obtained
 In addition, we also used medium resolution (30m) Landsat ETM+ images to calculate the
from the U.S. Geological Survey (USGS) to calculate slope through 3D analysis (Table 1). Land cover
 normalized difference vegetation index (NDVI) and a Digital Elevation Model (DEM 30) obtained
 from the U.S. Geological Survey (USGS) to calculate slope through 3D analysis (Table 1). Land cover
 types, NDVI and slope were used as input of MaxEnt model, which will be explained in Section 2.3.
 The Landsat ETM+ image was retrieved in summer 2016. The DEM 30 was retrieved in 2011. Park
 boundaries and entrances were manually mapped according to an online mapping platform
 (Amap.com) and validated through a ground-truth survey from October 14th to November 20th 2015

Remote Sens. 2020, 12, 3166 5 of 15

types, NDVI and slope were used as input of MaxEnt model, which will be explained in Section 2.3.
The Landsat ETM+ image was retrieved in summer 2016. The DEM 30 was retrieved in 2011.
Park boundaries and entrances were manually mapped according to an online mapping platform
(Amap.com) and validated through a ground-truth survey from 14 October to 20 November 2015 [74].
Since land cover and vegetation structure in parks did not experience significant change over recent
years, we believe that the time gap between different data acquisitions had little significant impact on
the results of the study.

 Table 1. Input variables for maximum entropy method (MaxEnt) model.

 Variable Explanation Data Source
 Bird distribution map Presence–absence dataset for each breeding guild Bird census survey
 Woodland, grassland, waterbody, pavements,
 Land cover type Landsat ETM+ image, 2016
 building, road, bare land

 NIR − R
 Normalized difference vegetation NDVI =
 NIR + R Landsat ETM+ image, 2016
 index (NDVI)
 NIR: reflectance value at the near infrared red band
 R: reflectance value at the red band
 Develop from Digital Elevation Model (DEM 30)
 Slope DEM 30, 2011
 through 3D analysis
 Distance to city center Direct distance from the site to city center Landsat ETM+ image, 2016
 Distance to waterbody Direct distance to the nearest waterbody Landsat ETM+ image, 2016

2.2.3. Birdwatching Record
 We obtained birdwatching records from the website of the China Birdwatching Record Center [75].
The China Birdwatching Record Center is a citizen science website where registered users can upload
birdwatching records. A typical record contains the following information: location, date, recorder
ID, weather, watching equipment, watching route, bird species and the number of bird individuals.
The most active time for the China Birdwatching Record Center website was 2008 to 2014. Then,
it experienced a decline with the growth of smartphone-based social media. We derived all the
birdwatching records submitted within the 102 parks between 1 January 2009 and 31 December 2013
and used the total number of birdwatching records in each park to represent its service for birdwatching.

2.3. Modeling Bird Habitats in Parks
 We used the maximum entropy method (MaxEnt) to estimate bird habitats in the 102 parks based
on results from the bird census survey and land cover dataset. In this study, we focused on habitats for
breeding birds to represent overall bird habitats because breeding birds have a relatively stable nest
location, territory and well-documented breeding traits (nest location and nest type, body length and
body weight) [26,67,76,77]. The point of bird occurrence is likely to be within their breeding territories,
making a strong link with the spatial properties of the habitat.
 First, we identified the breeding birds according to the bird checklist in Beijing [67]. Then,
we grouped them into different guilds by traits including body length, body weight, nest location and
nest type. Next, we ran the MaxEnt model to estimate distribution for each guild. Finally, we merged
the distribution maps for all guilds to generate a bird habitat map.
 In order to group breeding birds into different guilds, we input body length, body weight,
nest location and nest type data to draw a dendrogram in the RTM program [76,77]. We used the
Functional Diversity (FD) package to calculate the Gower distance matrix of these traits, which evaluated
the overall dissimilarity of species or taxa [78]. We chose the average method of hierarchical clustering
to generate a trait dendrogram. The branch lengths in the resulting dendrogram between clusters
were calculated by pairwise distances. We decided to use the distance of 0.18 as a threshold to balance
the number of guilds and their dissimilarity. As a result, we divided the breeding birds into thirteen

Remote Sens. 2020, 12, 3166 6 of 15

functional guilds (Figure 3) and generated a presence–absence dataset for each guild. We have included
the command syntax for R in the Supplementary Materials. Detailed steps on how to generate a
dendrogram can be found in this book [79]. Finally, we input the bird presence–absence dataset along
with land cover type, normalized difference vegetation index, slope, distance to city center and distance
to the nearest water body into the MaxEnt model (Table 1). It worth noting that the MaxEnt model
requires a minimum
 Remote Sens. sample
 2020, 11, x FOR PEERsize of 14—that is, birds of a guild observed at 14 sites or more. Therefore,
 REVIEW 6 of 16
we excluded the water surface open nest guild (observed at five sites) and the reed open nest guild
(observed at one site) from further analysis.

 Figure 3. Trait dendrogram for breeding guilds. Notes: The length of the branches in the vertical
 Notes: The length of the branches in the vertical direction indicates the dissimilarity from other species. At the
 direction Figure 3. Trait dendrogram forthe
 breeding guilds.
 bottom ofindicates the dissimilarity
 the dendrogram from
 is the species name. Theother species.
 red horizontal lineAt bottom
 indicates of the
 the threshold 0.18dendrogram
 to distinguish is the species
 breeding guilds. The numbers ❶–
 name. The red horizontal line indicates the threshold 0.18 to distinguish breeding guilds. The numbers
 Notes: The length of the branches in the vertical direction indicates the dissimilarity from other species. At the
 Ê–⓭ represent 13 breeding guilds identified by dendrogram.
 bottom of the dendrogram is the species name. The red horizontal line indicates the threshold 0.18 to distinguish
 breeding
 As a guilds.
 result, The the numbers
 MaxEnt ❶–⓭ modelrepresent
 generated 13 breeding
 a probabilityguildsmap identified by dendrogram.
 of distribution probability for each
guild at the resolution of 30 m × 30 m. We used 25% presence data for model testing. For each guild,
 Table 1. Input
we ran the Digital Elevation variables
 Model14 forto
 times maximum
 generateentropy method (MaxEnt)
 the distribution model.
 probability map. We assessed
the model accuracy by the threshold-independent evaluation statistic method. The threshold is the area
 Variable Explanation Data Source
under the curve (AUC) of the receiver’s operating characteristic [80]. After this, we defined habitat as
a distribution probability value higherPresence–absence dataset
 than 0.60. Then, wefor each breeding
 merged the suitable habitats for all the
 Bird distribution map Bird census survey
 guild and calculated the total area.
guilds (Table S3) to represent bird habitat
 Woodland, grassland, waterbody, pavements, Landsat ETM+
2.4.Land
 Analyses
 cover type
 building, road, bare land image, 2016
 We first conducted a correlation analysis between the number of birdwatching records and habitat
areas generated by MaxEnt modeling. We excluded parks − 
 NDVI = with no habitat or birdwatching record in
 + 
this analysis.
 Normalized difference vegetation Landsat ETM+
 Then, we used stepwise regression NIR:analysis
 reflectance
 tovalue at the near
 investigate howinfrared redinfluencing factors may
 possible
 index variations
explain (NDVI) in habitat area and birdwatching records. We conducted two stepwise image, 2016
 regression
 band
analyses with bird habitat area and the number of birdwatching records as dependent variables,
respectively. Independent variablesR:for reflectance value at the
 both regressions red bandthree aspects: park size and age,
 included
accessibility and urbanization degreeDevelop
 of a parkfrom
 andDigital Elevation Model
 its surrounding (DEM
 area and the30)
 landscape composition
 Slope DEM 30, 2011
and configuration in a park (Table S2). In addition,
 through 3D analysiswe added bird habitat area as an independent
variable in the regression which had the number of birdwatching records as a dependent variable.
 Landsat ETM+
WeDistance to city centerbetween the variables
 tested collinearity Direct distance from the site
 by correlation to city and
 analysis center
 variance inflation coefficient
(VIF). The results were included in Tables S4 and S5, respectively. Stepwise regression image, 2016
 was able
to identify independent variables that were highly correlated and the final model Landsat
 only ETM+
 contained
 Distance to waterbody
independent Direct
 variables with a relatively lowdistance
 degreetoofthe nearest waterbody
 autocorrelation. image, 2016

 As a result, the MaxEnt model generated a probability map of distribution probability for each
 guild at the resolution of 30 m × 30 m. We used 25% presence data for model testing. For each guild,
 we ran the Digital Elevation Model14 times to generate the distribution probability map. We assessed
 the model accuracy by the threshold-independent evaluation statistic method. The threshold is the

Remote Sens. 2020, 12, 3166 7 of 15

 Finally, we conducted a one-way analysis of variance (ANOVA) to investigate the differences
between parks with and without birdwatching records (Table S2). We separated parks with and
without birdwatching records into two groups and compared them to determine whether any of the
variables in terms of park size and age, accessibility, urbanization degree and landscape composition
and configuration were significantly different across the two groups. All the analyses were conducted
in SPSSTM . Additionally, we used ArcGISTM 10.0 to visualize the spatial distributions of bird habitat
area and the number of birdwatching records.

3. Results

3.1. Bird Census and Modeling
 We found 79 species from the bird survey, among which 53 were breeding species. We identified
13 functional guilds for the 53 breeding species based on their breeding traits (Table S3). As the MaxEnt
model requires a minimum sample size of 14, 11 out of the 13 functional guilds were entered into the
model. The model passed the AUC accuracy test with the values ranging from 0.71 to 0.85 (Table S6).
More details on the accuracy test are included in the Supplementary Materials.

3.2. Spatial Patterns and Relationships of Park’s Bird Habitats and Birdwatching Records
 Our results showed that 61 parks had habitats at least for one breeding guild. The area of habitat
in each park ranged from 0.09 ha to 169 ha, with an average of 17 ha. The habitat proportion ranged
from 0.45% to 71%, with an average of 19%. Most parks with large bird habitat areas were located in
the central and northern regions of the city (Figure 4a). The top five parks with large bird habitats areas
Remote
are Sens. 2020,
 Summer 11, x FOR
 Palace, PEER REVIEW
 Olympic Forest Park, Yuanmingyuan Park, Heaven Temple and Chaoyang Park. 8 of 16

 Figure 4. Bird habitat area and number of birdwatching records in parks of Beijing. (a) Spatial
Figure 4. Bird habitat area and number of birdwatching records in parks of Beijing. (a) Spatial distributions
 distributions of bird habitat area and number of birdwatching records; (b) the numeric relationship
of bird habitat area and number of birdwatching records; (b) the numeric relationship between bird habitat
 between bird habitat area and number of birdwatching records.
area and number of birdwatching records.
 The China Birdwatching Record Center had 330 birdwatching records in 26 parks from 2009 to
3.3. Park
2013. ForCharacteristics Explaining
 individual parks, Variations
 the record numberinranged
 Bird Habitat
 from 1Area
 to 75,and Birdwatching
 with a mean value Activities
 of 12. Most parks
with The stepwise
 lots of regression
 birdwatching results
 records areshowed that
 located in thepark size,and
 middle age,theproportion
 northwest ofof
 pavement, landscape
 the city (Figure 4b).
connectedness,
The pavements
 top five parks with highlargest patch index
 birdwatching recordsand woodland
 were Olympicpatch Forestdensity (Table 2)
 Park, Heaven explained
 Temple, 95%
 Summer
of the variation
Palace, in bird Park
 Yuanmingyuan habitat
 andarea. Parks
 Black that are
 Bamboo larger
 Park, fourand older and
 of which werehave
 alsoless pavement,
 listed as the topbetter
 five
connected
parks with landscapes and less
 large bird habitat woodland
 areas patches
 (Figure 4a). Amongtendthe
 to 26
 have
 parkslarger
 withareas of suitablerecords,
 birdwatching bird habitats.
 22 had
 In terms(0.15–169
bird habitats of the number
 ha). Ourofresults
 birdwatching records,
 showed that our habitat
 the bird results area
 showed that park size
 was significantly and the
 associated
woodland
with landscape
 the number shape indexrecords
 of birdwatching (r =explained
 together 0.828, p

Remote Sens. 2020, 12, 3166 8 of 15

3.3. Park Characteristics Explaining Variations in Bird Habitat Area and Birdwatching Activities
 The stepwise regression results showed that park size, age, proportion of pavement, landscape
connectedness, pavements largest patch index and woodland patch density (Table 2) explained 95%
of the variation in bird habitat area. Parks that are larger and older and have less pavement, better
connected landscapes and less woodland patches tend to have larger areas of suitable bird habitats.

 Table 2. Results of stepwise regression models of factors that may impact bird habitat area and number
 of birdwatching records in parks.

 Dependent Variable
 Independent Variable Area of Bird Habitat (n = 61) Number of Birdwatching Records (n = 26)
 Standardized Coefficient Significance Standardized Coefficient Significance
 Park size 1.011

PLAND_woodland 56.05 70.50
 Landscape
 PLAND_pavements 6.73 9.21
 composition and
 PD_woodland 156.85 239.89
 configuration
 ED_waterbody —— ——
 We used
Remote1Sens. one-way
 2020, 12, 3166 ANOVA to identify the differences between parks with birdwatching and parks9 of 15
 without birdwatching. n represents the number of parks.

 Figure 5. Comparisons of park size with/without birdwatching records. Notes: park with no birdwatching,
 nFigure 5. Comparisons
 = 76, park of (ha),
 sizemean = 24.87 park park
 size with
 with/without birdwatching
 birdwatching, records.
 n = 26, park sizemeanNotes:
 = 86.37park
 (ha). with no
 birdwatching, n = 76, park size mean = 24.87 (ha), park with birdwatching, n = 26, park size mean = 86.37
4. Discussion
 (ha).

4.1. Spatial Patterns of Parks with Bird Habitats and Birdwatching Record
 4. Discussion
 Our results indicated that parks with relatively more habitats and/or birdwatching activities
 4.1. Spatial
clustered Patterns
 along of Parks with
 the “central axis”,Bird Habitats
 which startedandfrom
 Birdwatching RecordCity (the center of Beijing in both
 the Forbidden
ancient and
 Our modern
 results times), stretched
 indicated that parks allwith
 the way to the north
 relatively more and ended
 habitats in thebirdwatching
 and/or Olympic Forestry Park
 activities
(designed with a dragon-shaped waterbody to echo the Forbidden City). Parks along the central
 clustered along the “central axis”, which started from the Forbidden City (the center of Beijing in both axis
often have
 ancient and historical
 modernand cultural
 times), legacies
 stretched allthat
 thecan
 way betotraced back to
 the north anddynasties.
 ended inFor
 theexample,
 Olympicthe Beihai
 Forestry
Park was built in 1163 and has served as an imperial home and garden since 1179. The Beijing Zoo
was built in the Qing Dynasty upon royal gardens. Researchers found that high density of old trees
contributes to bird richness [24], which could explain the connections between ancient parks and
suitable bird habitats. In contrast, parks between the third and the fifth ring roads were built more
recently, provided less bird habitats and attracted less birdwatchers.
 Results showed that numbers of birdwatching records in parks located in the northwest area
were high. The northwest area in Beijing hosts the IT companies (known as the Chinese “Silicon
Valley”), quite a few well-known universities and residents with good education (Figure 4). The spatial
overlap between highly educated residents and parks with active birdwatching activities may not be a
coincidence. Birdwatchers usually have a relatively high education level [70]. Moreover, living close
to parks tended to increase birdwatching and interacting with birds [15,17,32]. The spatial overlaps
observed in this study confirmed these previous findings regarding the education level of birdwatchers
and “nearby birdwatching” in urban areas.

4.2. Relationship between Parks with Bird Habitats and Birdwatching Record
 We found the area of bird habitat was the most important independent variable explaining the
variation in the number of birdwatching records. Habitat area is usually considered to be associated
with bird species richness [30], so this finding is consistent with previous studies which found
that bird species richness is a major indicator of locations where birdwatching activities may take
place [17,35,36]. With that being said, bird habitat was clearly not a sufficient condition to attract

Remote Sens. 2020, 12, 3166 10 of 15

birdwatchers. Among the 66 parks with bird habitats, only 22 of them had birdwatching records.
Accessibility played an important role in attracting birdwatchers. Our analysis showed that parks with
birdwatching records were closer to the downtown area with more roads, bus stops and metro stations
nearby, compared with parks with no birdwatching records.
 Interestingly, bird habitat was not a necessary condition for birdwatching either. Among the
26 parks with birdwatching records, four of them were estimated to have few bird habitats according
to our results. We did not conduct the one-way ANOVA analysis to examine differences in park
characteristics’ due to the small sample size. These four parks were all relatively small (0.8 to 8 ha)
compared to the average park size of 41 ha. We might underestimate bird habitat in the four small
parks due to the limitation of MaxEnt modeling, which will be discussed later in the limitation section.
In addition, there are other parks within a 1-mile radius of these four parks, which may provide
habitats for birds. Lastly, birdwatching records indicated that birds which appeared in the four
parks were Eurasian Tree Sparrow (Passer montanus), Common Magpie (Pica pica), Azure-winged
Magpie (Cyanopica cyanus), Large-billed Crow (Corvus macrorhynchos) and Carrion Crow (Corvus corone).
These species are resident birds well adapted to urban environments, which contain a large proportion
of impervious surfaces (such as buildings, pavements and roads). Future on-site study is needed to
investigate parks with few habitats hosting birdwatchers by using high-resolution spatial data.
 It is also worth noting that environmental non-government organizations (NGOs) played an
essential role in introducing and promoting birdwatching activities [18,70]. The three parks with the
most birdwatching records all host birdwatching activities organized by NGOs on a regular basis.

4.3. Impacts of Park Characteristics on Bird Habitats and Birdwatching
 Results showed that park size was an important factor. Larger parks tend to have more bird
habitats and birdwatching records. Park size is commonly used as an indicator to describe the quality
of a park in studies evaluating their attractiveness to people as well as their ecosystem services
in general [81,82]. Our findings provide evidence that parks with larger sizes contribute to both
supporting bird species as well as hosting birdwatching activities. Despite these benefits of large parks,
it is not always feasible to enlarge a park. Our results indicate that certain landscape patterns, such as
less pavements, may increase bird habitats and birdwatching records, which could be achieved through
proper design. Previous studies also found that larger parks usually have high bird richness [9,16,21]
and also found that less pavements can promote bird richness [29].
 Furthermore, our results also showed that accessibility determined whether a park would be
attractive to birdwatchers, which is consistent with previous studies conducted in protected areas and
national parks [15,17]. We found that small parks with limited bird habitats but good accessibility
still hosted some birdwatching activities. For example, Liuyin Park is 15 ha in size, with 3 ha bird
habitat (compared with the mean values of 41 and 10, respectively). With a score of 51 in accessibility
(number of metro and bus stops, compared with the mean value of 31), it contributed six out of
330 birdwatching records. So, increasing the number of bus stops and subway stations can help to
attract more birdwatchers.

4.4. Limitations
 This study has several limitations. Firstly, we only considered birdwatchers for the benefits
brought by birds. Park visitors who did not intend to watch birds or submit birdwatching records may
also enjoy the presence of birds in parks, but they were not considered in this approach. Furthermore,
using the number of birdwatching records may oversample areas with rare or exotic birds. For example,
the Bluethroat (Luscinia svecica) appeared in the Olympic Forest Park and attracted three records within
four days between May 16 and 19, 2010. Future studies should consider the possibilities of eliminating
such impact, such as surveying visitors to estimate how they enjoy birds in parks.
 Secondly, the MaxEnt model worked on a resolution of 30m * 30m and was not able to capture
habitats smaller than this scale. This may result in an underestimation of the total bird habitat area

Remote Sens. 2020, 12, 3166 11 of 15

in small parks. While it did generate habitat estimates successfully in the 11 out of 33 parks smaller
than 8 ha (Table S7), four parks smaller than 8 ha that reported birdwatching records were estimated
to have few habitats by the model. Future studies should investigate what causes the inconsistency
between suitable bird habitats and birdwatching records in the parks as well as apply high-resolution
data and combine them with small-scale surveys to make the habitat estimation of small-scale parks
more accurate.
 Lastly, we employed land cover based on high-resolution remote sensing data as input for the
MaxEnt model to estimate bird habitats in this study. Therefore, we carry the limitations of remote
sensing images, which hardly detect the vertical vegetation structure, such as conditions of shrubs
under arbor cover and length or quality of grass. Such 3D vegetation structures that are captured by
remote sensing data may impact bird habitats [9,16,21,83].

5. Conclusions
 We investigated the factors that impact bird habitats and birdwatching activities in urban parks of
Beijing. Our results showed that parks with large areas and a long history tend to provide more bird
habitats and birdwatching services. Besides size and age, park landscape pattern also has an impact
on bird habitat area. Parks with less pavement cover, less clustered pavements and better-connected
landscapes tend to have more bird suitable habitats. Birdwatching activities were highly associated
with suitable bird habitats, as the area of habitat can explain 65% of the variation in birdwatching
records. In addition, park location and accessibility also contributed to the popularity of a park as a
birdwatching destination. Providing habitats in cities and connecting urban residents with nature are
the fundamental goals of urban parks. Here, we focus on birds and birdwatching to examine what
factors may help or impede parks in fulfilling these objectives. Our findings will inform park planners
and managers about strategies and measures that can help them better achieve one or both goals.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/19/3166/s1,
The command syntax of drawing a trait dendrogram in RTM . Table S1. Location, size, age and waterbody of
sample parks, Table S2. Independent variables for statistical analysis, Table S3. Name list of breeding guilds,
Table S4. Correlation matrix of landscape index, Table S5. The variance inflation coefficient (VIF) of index in
regression, Table S6. The area under the curve (AUC) of each guild habitat assessment in MaxEnt model, Table S7.
Parks with bird habitats of less than 1.6 ha.
Author Contributions: Conceptualization, G.H.; methodology, Z.Z., and G.H.; software, Z.Z.; validation, G.H.;
formal analysis, Z.Z.; investigation, Z.Z.; resources, G.H.; data curation, Z.Z.; writing—original draft, Z.Z.;
writing—review and editing, G.H.; visualization, Z.Z.; supervision, G.H.; project administration, G.H.; funding
acquisition, G.H. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the National Natural Science Foundation of China (31670702).
Acknowledgments: We thank Tianhui Wang (Beijing Forestry University) for the graphic abstract materials.
We appreciate constructive suggestions from both anonymous reviewers and academic editors.
Conflicts of Interest: The authors declare no conflict of interest.

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